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About This Role
Job Summary:
Do you have a passion for Artificial Intelligence (AI) and are looking to make a difference in how the latest clinical trials progress into the market?
Do you have a background in data science and/or computer science with experience working with AI tools for interactive AI applications across various IT systems?
If so, we have an exciting opportunity for you to help take the reins of a groundbreaking project at Medpace.
We are currently seeking a professional with direct experience interacting with artificial intelligence (AI) tools such as NLP, LLM, IA etc. This professional will be expected to lead the programming and fine\-tuning of various AI tools and help IT teams to implement them into new applications.
This professional will work collaboratively across the organization with multiple teams to help design the roadmap for how AI can improve efficiency of company processes and provide insights and assistance to users. Applicants should have sufficient technical skills to help lead these types of AI projects independently and to train technical teams for support.
Responsibilities :
- Lead the ideation, research, PoC programming and then support programming and fine\-tuning of various AI tools with the IT team to implement them into new applications;
- Lead the identification and development of AI tools such as NLP, LLM, and IA;
- Design, develop, and implement artificial intelligence (AI) models and algorithms for various applications;
- Work on a broad set of tasks encompassing various forms of machine intelligence (e.g., machine learning, algorithms, neural networks, computer vision, robotics) to develop AI models for specific applications;
- Train AI models using various algorithms and techniques, optimizing for accuracy, efficiency, and interpretability;
- Deploy AI solutions into production environments, ensuring scalability, reliability, and integration with existing systems;
- Continuously improve models based on feedback and performance metrics;
- Lead AI team activities in educating Medpace AI users in the best development, training and deployment of AI tools including various forms of machine intelligence; and
- Participate in educating, training and development of more junior team members.
Qualifications :
- PhD in Artificial Intelligence, Computer or Data Science, or related field;
- Preferably several years of experience working with different AI capabilities and showcasing your passion both at work and outside work in the development of highly complex AI models (NLP, LLMs, Deep learning etc);
- Technical proficiency in programming languages and frameworks commonly used in NLP and AI (e.g., Python, TensorFlow, PyTorch);
- Excellent communication skills to collaborate effectively with cross\-functional teams;
- Demonstrated ability to lead projects independently and mentor technical teams;
- A passion for staying up\-to\-date with the latest advancements in NLP and AI technologies; and
- Analytical thinker with great attention to detail.
Medpace Overview :
Medpace is a full\-service clinical contract research organization (CRO). We provide Phase I\-IV clinical development services to the biotechnology, pharmaceutical and medical device industries. Our mission is to accelerate the global development of safe and effective medical therapeutics through its scientific and disciplined approach. We leverage local regulatory and therapeutic expertise across all major areas including oncology, cardiology, metabolic disease, endocrinology, central nervous system, anti\-viral and anti\-infective. Headquartered in Cincinnati, Ohio, employing more than 6,000 people across 40\+ countries.
Why Medpace?:
People. Purpose. Passion. Make a Difference Tomorrow. Join Us Today.
The work we’ve done over the past 30\+ years has positively impacted the lives of countless patients and families who face hundreds of diseases across all key therapeutic areas. The work we do today will improve the lives of people living with illness and disease in the future. Cincinnati Perks* Cincinnati Campus Overview
- Flexible work environment
- Competitive PTO packages, starting at 20\+ days
- Competitive compensation and benefits package
- Company\-sponsored employee appreciation events
- Employee health and wellness initiatives
- Community involvement with local nonprofit organizations
- Discounts on local sports games, fitness gyms and attractions
- Modern, ecofriendly campus with an on\-site fitness center
- Structured career paths with opportunities for professional growth
- Discounted tuition for UC online programs
Awards* Named a Top Workplace in 2024 by The Cincinnati Enquirer
- Recognized by Forbes as one of America's Most Successful Midsize Companies in 2021, 2022, 2023 and 2024
- Continually recognized with CRO Leadership Awards from Life Science Leader magazine based on expertise, quality, capabilities, reliability, and compatibility
What to Expect Next
A Medpace team member will review your qualifications and, if interested, you will be contacted with details for next steps.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Medpace, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Medpace AI Hiring
Medpace has 5 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer. Based in Cincinnati, OH, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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